Patterns Of Oriented Edge Magnitudes Research Articles

Color-compensated multi-scale exposure fusion based on physical features

The dynamic range captured by ordinary digital cameras is much smaller than that in natural scenes, so a single image cannot fully capture the real scene details when shooting high dynamic scenes. Traditional multi-exposure image fusion algorithms often cause inconsistencies in local brightness and corresponding scenes, which easily leads to problems such as color distortion and loss of details. Based on this, a novel multi-scale exposure fusion approach based on physical features is presented in this paper. Specifically, we first employ a novel Retinex model to get the illumination maps of original images and adopt the obtained illuminations map to construct the exposure maps. Then, a feature descriptor named patterns of oriented edge magnitudes (POEM) is introduced to extract local contrast from source images. Moreover, the extracted feature is combined to construct a weight map, and the weight term is integrated with the Laplacian pyramid algorithm to obtain an initial fusion image. Finally, simple detail enhancement and color compensation operations are performed to get the final fusion result. The numerous experiments have revealed that the proposed approach yields comparative and even better results in comparison with some state-of-the-art techniques both in subjective and objective evaluation.

Vehicle logo recognition based on overlapping enhanced patterns of oriented edge magnitudes

Vehicle logo recognition (VLR) has attracted wide attention from the community of intelligent transportation systems (ITS) due to its important role. Although many methods have been proposed for VLR, it remains a challenging problem. In this paper, we present a novel method for VLR. Our method includes (1) observation of the local anisotropy of vehicle logo images; (2) adoption of the idea of patterns of oriented edge magnitudes (POEM) and an advanced version of POEM for vehicle logo feature description called overlapping enhanced POEM (OE-POEM); (3) implementation of whitened principal component analysis (WPCA) for feature dimension reduction followed by collaborative-representation-based classification (CRC) as a classifier to perform VLR. We also construct a new vehicle logo dataset (HFUT-VL), which is larger and more comprehensive than the existing vehicle logo datasets. Finally, we conduct experiments on HFUT-VL, and the results indicate state-of-the-art VLR performance.

Facial Feature Localization Using Robust Active Shape Model and POEM Descriptors

Active Shape Model (ASM) has been shown to be a powerful tool for image interpretation, especially in feature points localization for face image. The original ASM model parameter estimation is based on the assumption that the profiles follow a Gaussian distribution. Its performance is always vulnerable to distortion due to pose, illumination and expression variations. In this paper, the improvement of ASM model concerns the following two aspects. Firstly, an adaptive parameter estimation method are proposed by defining a rotation factor. Secondly, local appearances of landmarks are originally represented by Patterns of Oriented Edge Magnitudes (POEM) descriptors, which can provide more robust and accurate guidance for searching than intensity profiles. The simulations are carried out using the IMM dataset, which contains 240 face images. Experimental results show that the proposed method significantly outperforms the original ASM and ASM plus LBP method under exterior variations.

Exploring Patterns of Gradient Orientations and Magnitudes for Face Recognition

A novel direction for efficiently describing face images is proposed by exploring the relationships between both gradient orientations and magnitudes of different local image structures. Presented in this paper are not only a novel feature set called patterns of orientation difference (POD) but also several improvements to our previous algorithm called patterns of oriented edge magnitudes (POEM). The whitened principal component analysis (PCA) dimensionality reduction technique is applied upon both the POEM- and POD-based representations to get more compact and discriminative face descriptors. We then show that the two methods have complementary strength and that by combining the two algorithms, one obtains stronger results than either of them considered separately. By experiments carried out on several common benchmarks, including the FERET database with both frontal and nonfrontal images as well as the very challenging LFW data set, we prove that our approach is more efficient than contemporary ones in terms of both higher performance and lower complexity.

Face recognition using the POEM descriptor

Real-world face recognition systems require careful balancing of three concerns: computational cost, robustness, and discriminative power. In this paper we describe a new descriptor, POEM (patterns of oriented edge magnitudes), by applying a self-similarity based structure on oriented magnitudes and prove that it addresses all three criteria. Experimental results on the FERET database show that POEM outperforms other descriptors when used with nearest neighbour classifiers. With the LFW database by combining POEM with GMMs and with multi-kernel SVMs, we achieve comparable results to the state of the art. Impressively, POEM is around 20 times faster than Gabor-based methods.

Enhanced Patterns of Oriented Edge Magnitudes for Face Recognition and Image Matching

A good feature descriptor is desired to be discriminative, robust, and computationally inexpensive in both terms of time and storage requirement. In the domain of face recognition, these properties allow the system to quickly deliver high recognition results to the end user. Motivated by the recent feature descriptor called Patterns of Oriented Edge Magnitudes (POEM), which balances the three concerns, this paper aims at enhancing its performance with respect to all these criteria. To this end, we first optimize the parameters of POEM and then apply the whitened principal-component-analysis dimensionality reduction technique to get a more compact, robust, and discriminative descriptor. For face recognition, the efficiency of our algorithm is proved by strong results obtained on both constrained (Face Recognition Technology, FERET) and unconstrained (Labeled Faces in the Wild, LFW) data sets in addition with the low complexity. Impressively, our algorithm is about 30 times faster than those based on Gabor filters. Furthermore, by proposing an additional technique that makes our descriptor robust to rotation, we validate its efficiency for the task of image matching.